Find research datasets worth reusing

opencc-by-4.0Jul 2019View details →

Dataset of predicted dynamic specific yield under periodic water table oscillations

This dataset accompanies the submitted paper in the Water Resources Research: An approximate model for predicting the specific yield under periodic water table oscillations. The dataset includes 299 numerical experiments on predicting dynamic specific yield with different soil properties and different amplitudes of groundwater level oscillations. With this dataset, we proposed an approximate model for predicting the specific yield under diurnal and seasonal water level fluctuations.

opencc-by-4.0Jul 2019View details →

T2* and quantitative susceptibility mapping in an equine model of post-traumatic osteoarthritis: prediction of mechanical and structural properties

Dataset for the manuscript titled "T2* and quantitative susceptibility mapping in an equine model of post-traumatic osteoarthritis: assessment of mechanical and structural properties"

opencc-by-4.0Jul 2019View details →

Supplementary File S2 for the publication 'Predicting Bacterial Virulence Factors - Evaluation of Machine Learning and Negative Data Strategies' by Rentzsch, R et al.

Supplementary File S2 for the publication 'Predicting Bacterial Virulence Factors - Evaluation of Machine Learning and Negative Data Strategies' by Robert Rentzsch, Carlus Deneke, Andreas Nitsche, and Bernhard Y. Renard

opencc-by-4.0Nov 2018View details →

Dataset from 'Klever, L., Voudouris, D., Fiehler, K., & Billino, J. (2019). Age effects on sensorimotor predictions: What drives increased tactile suppression during reaching? Journal of Vision, 19(9):9, 1-17. doi:10.1167/19.9.9

Dataset associated with the following publication: Klever, L., Voudouris, D., Fiehler, K., & Billino, J. (2019). Age effects on sensorimotor predictions: What drives increased tactile suppression during reaching? Journal of Vision. -------------------------------------------------------------- We provide two files; one data file contains data on which analyses are based, the other one gives the column labels for the data file. ------------------------------------------------------------- For further questions, please contact: lena.klever[at]psychol.uni-giessen.de

opencc-by-4.0Aug 2019View details →

GCTB SBayesR shrunk sparse linkage disequilibrium matrices for HM3 variants, summary statistics and predictors generated from "Improved polygenic prediction by Bayesian multiple regression on summary statistics" by Lloyd-Jones, Zeng et al. 2019.

GCTB LD matrices and results for HapMap 3 variants and 2.8M variants, which were used for simulation, cross-validation and across biobank analyses in the manuscript "Improved polygenic prediction by Bayesian multiple regression on summary statistics" by Lloyd-Jones, Zeng et al. 2019. Unzip and see README for further details.

opencc-by-4.0Nov 2018View details →

Prediction and Rehabilitation of Highway Embankment Slope Failures in Changing Climate

Corresponding data set for Tran-SET Project No. 17GTLSU04. Abstract of the final report is stated below for reference: "Highway slopes constructed with clayey soil are prone to desiccation cracks due to wetting and drying weather cycle, which allows greater moisture infiltration into the embankment from precipitation. Fissures formed due to extended wetting and drying cycles allow water to seep deeper into the soil than surficial wetting and increase the water content. This increases the moisture content in the soil and results in reduction in shear strength to the fully softened strength. On the other hand, development of desiccation cracks and reduction of the soil matric suction ultimately result in higher hydraulic conductivity value which causes development of higher pore water pressure. As the moisture content of the clayey soil increases, the strength reduces to a fully softened shear strength that causes frequent shallow and medium slope failures that are oriented approximately parallel to the surface of the embankment. Hence, the fully softened strength of Louisiana and Texas soils need to be quantified to develop a predictive tool for identifying high-risk zones of highway embankments. Given the documented failures in Texas and Louisiana, this research project is focused on investigating past failures to develop lessons learned and guidelines that can be implemented in the predictive framework."

openmit-licenseSep 2019View details →

Solution #4 for Predicting Molecular Properties Kaggle Competition

Code and additional data for solution #4 in <a href="https://www.kaggle.com/c/champs-scalar-coupling/overview">Predicting Molecular Properties</a> competition, described in <a href="https://www.kaggle.com/c/champs-scalar-coupling/discussion/106534#latest-613848">#4 Solution [Hyperspatial Engineers]</a>.

Trained MNIST predictions for label shift experiments

Trained MNIST predictions for label shift experiments: https://github.com/kundajelab/label_shift_experiments/

data set related to article Simultaneous and sequential subitizing are separate systems, and neither predicts math abilities

This record contains raw data related to article Simultaneous and sequential subitizing are separate systems, and neither predicts math abilities

Channel State Information (CSI) analysis for predictive maintenance using Convolutional Neural Network (CNN)

Dataset manual: This dataset contains CSI amplitude values for rotating motors in an office environment. Details of the experiments may be found in the corresponding paper published in the DATA'19 workshop, SenSys (<a href="https://doi.org/10.1145/3359427.3361917">https://doi.org/10.1145/3359427.3361917</a>).  Folder structure: The folders for servo motor and stepper motor contains separate folders for network reconnection conditions (w_recc: with reconnections, wo_recc: without recconnections) and load conditions (w_load: with load and wo_load: without load). The data is stores as Matlab files with .mat extentions.  File structure: In each file name, the digits after the '_' at the end of the file name correspond to the speed of the motor. In case of stepper motor these numbers could be directly interpreted as rpm. Ex: table_inj_with_load_5_0.mat corresponds to stationary motor (0 rpm) and table_inj_with_load_5_250.mat corresponds to motor rotating with 250 rpm speed. In the case of servo motor these numbers should be mapped with the following table in order to get the speeds. 0: 0 rpm 50: 14.45 rpm 100: 8.02 rpm 150: 5.38 rpm 200: 4.05 rpm 250: 3.26 rpm 300: 2.67 rpm Ex: table_inj_with_load_50.mat corresponds to motor running with 14.45 rpm. Each file has 3 columns, each corresponding to CSI value, labels (speed/last digits in the file name) and the data sample number (not in sequence as a result of packer loss) respectively. CSI values are typically a matrix of size 3000*180 (3000 CSI samples for 3sec data @1kHz sampling rate and 180 channels for 6 antenna pairs @ 30 subcarrier data per antenna).

Fundamental Limits From Chaos On Instability Time Predictions In Compact Planetary Systems

REBOUND SimulationArchives analysed in Hussain & Tamayo (2019) Fundamental Limits From Chaos On Instability Time Predictions In Compact Planetary Systems. Dataset consists of a set of compact planetary configurations, and large ensembles of shadow trajectories from N-body simulations with initial conditions perturbed near machine precision to investigate the spread in instability times. Instructions and scripts for extracting the data and generating the plots in the paper can be found at https://github.com/Naireen/StabilitySetImage

The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

Data and figures presented in the paper "The ESCAPE project: Energy-efficient scalable algorithms for weather prediction at exascale". The discussion paper is available at: https://doi.org/10.5194/gmd-2018-304

Simulated and experimental data distributed to the CASP13 participants in protein structure prediction assisted with sparse NMR data

All simulated and experimental data distributed to the CASP participants in protein structure prediction assisted with sparse NMR data in CASP13. Also available at http://predictioncenter.org/casp13/results.cgi?view=targets&model=first&tr_type=others&sub_type=N&groups_id=

Speed Prediction in Large and Dynamic Traffic Sensor Networks

Aggregated traffic sensor data from Fortaleza (Brazil) in 2014. Please cite the following paper when using the dataset: R.P. Magalhaes, F. Lettich, J.A. Macedo, F.M. Nardini, R. Perego, C. Renso, R. Trani., Speed prediction in large and dynamic traffic sensor networks, Information Systems (2019) 101444, <a href="https://doi.org/10.1016/j.is.2019.101444">https://doi.org/10.1016/j.is.2019.101444</a> You can also check details regarding the dataset in the paper.

Data set related to the manuscript "On the development of an original mesoscopic model to predict the capacitive properties of carbon-carbon supercapacitors"

Graphical files in the agr format for all the figures in the main text of the manuscript entitled "On the development of an original mesoscopic model to predict the capacitive properties of carbon-carbon supercapacitors" (<a href="https://doi.org/10.1016/j.electacta.2019.135022">10.1016/j.electacta.2019.135022</a>).

Ligand-induced Conformational Selection Predicts the Selectivity of Cysteine Protease Inhibitors - apo and validation MD

Supplementary data of "Ligand-induced Conformational Selection Predicts the Selectivity of Cysteine Protease Inhibitors" paper. This dataset consists of molecular dynamics simulations trajectories and topology of Cruzain, Cathepsin K and Cathepsin L enzymes in it apo form, together with validation simulations. We ran five replicate 100ns simulations on each complex, with randomized initial velocities.

Ligand-induced Conformational Selection Predicts the Selectivity of Cysteine Protease Inhibitors - ICL and IKR complexes MD

Supplementary data of "Ligand-induced Conformational Selection Predicts the Selectivity of Cysteine Protease Inhibitors" paper. This dataset consists of the molecular dynamics simulations trajectory and topology of Cruzain, Cathepsin K and Cathepsin L enzymes in noncovalent and covalent complexes with selective ligand against Cruzain (ICL* and CCL) and Cathepsin K (IKR and CKR). We ran five replicate 100ns simulations on each complex, with randomized initial velocities.

Ligand-induced Conformational Selection Predicts the Selectivity of Cysteine Protease Inhibitors - ICR and ICK complexes MD

Supplementary data of "Ligand-induced Conformational Selection Predicts the Selectivity of Cysteine Protease Inhibitors" paper. This dataset consists of molecular dynamics simulations trajectory and topology of Cruzain, Cathepsin K and Cathepsin L enzymes in noncovalent and covalent complexes with selective ligand against Cruzain (ICR and CCR) and Cathepsin K (ICK and CCK). We ran five replicate 100ns simulations on each complex, with randomized initial velocities.

Ligand-induced Conformational Selection Predicts the Selectivity of Cysteine Protease Inhibitors - Inputs and Analysis

Supplementary data of "Ligand-induced Conformational Selection Predicts the Selectivity of Cysteine Protease Inhibitors" paper. This dataset consists of the parametrized ligand (covalent and noncovalent form of ICR, ICK, ICL, IKR) and complexes files, sample of input files used for Molecular dynamics simulations and analysis procedures, and the raw data of results.

In silico prediction of high-resolution Hi-C interaction matrices (part III)

The uploaded files are source datasets for the HiC-Reg approach. HiC-Reg is a regression based method that predict contact counts from one-dimensional regulatory signals such as epigenetic marks and regulatory protein binding. See more details here (<a href="https://github.com/Roy-lab/HiC-Reg">https://github.com/Roy-lab/HiC-Reg</a>). There are a total of six files in this dataset: Data.tgz, Gm12878.tgz, Hmec.tgz, K562.tgz, Huvec.tgz and Nhek.tgz. The Data.tgz include predictions and other downstream analysis such as feature importance analysis, significant interaction calling, and data files for select figures. The Gm12878.tgz, K562.tgz, Huvec.tgz, Hmec.tgz and Nhek.tgz contain trained models, predictions, feature files for two chromosomes for in each cell line. This is part III of the dataset which contains Nhek.tgz and Data.tgz.